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Titlebook: Artificial Intelligence Research; Third Southern Afric Anban Pillay,Edgar Jembere,Aurona Gerber Conference proceedings 2022 The Editor(s) (

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发表于 2025-3-21 16:33:48 | 显示全部楼层 |阅读模式
期刊全称Artificial Intelligence Research
期刊简称Third Southern Afric
影响因子2023Anban Pillay,Edgar Jembere,Aurona Gerber
视频videohttp://file.papertrans.cn/163/162135/162135.mp4
学科分类Communications in Computer and Information Science
图书封面Titlebook: Artificial Intelligence Research; Third Southern Afric Anban Pillay,Edgar Jembere,Aurona Gerber Conference proceedings 2022 The Editor(s) (
影响因子This book constitutes the refereed proceedings of the Third Southern African Conference on  Artificial Intelligence Research, SACAIR 2022, held in Stellenbosch, South Africa, in  December 2022. .The 26 papers presented were thoroughly reviewed and selected from the 73 submissions. They are organized on the topical sections on​ algorithmic, data driven and symbolic AI; socio-technical and human-centered AI; responsible and ethical AI. 
Pindex Conference proceedings 2022
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发表于 2025-3-21 22:24:41 | 显示全部楼层
H. U. E. Thoden van Velzen,W. van Weteringtions required, but at a cost to generalisation. Moreover, naively regularising the Jacobian norm can make the ODE system more ill-conditioned, contrary to what is believed in the literature. As an alternative, we regularise the condition number of the Jacobian and observe a lower number of function
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Other Peoples’ Religions are Absurd has on the in-game playstyle classification’s accuracy, prediction stability, and generalisability. We apply and test our framework using MiniDungeons, a rogue-like dungeon exploration game, and further evaluate our framework using a natural dataset containing human player action data from the plat
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Gloria Gabbianelli,Agnese Formicastically significant improvement in prediction accuracy, which confirms the effectiveness of including auxiliary information in a context-aware recommendation system. We report a 4% and 11% increase in the NDCG score for long and short user sequence datasets, respectively.
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https://doi.org/10.1007/978-3-662-36733-9amely boredom, confusion, frustration, and engagement. Dataset for Affective States in E-Environment (DAiSEE) was used to train, validate, and test the baseline model, which reported an accuracy of 86% on 4305 test samples. In the next stage, mappings between estimated affective states and learning
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